The recent emergence of reinforcement learning (RL) has created a demand for robust
statistical inference methods for the parameter estimates computed using these algorithms …

Thompson sampling (TS) is widely used in sequential decision making due to its ease of use
and appealing empirical performance. However, many existing analytical and empirical …

One approach to guaranteeing safety in Reinforcement Learning is through cost constraints
that are dependent on the policy. Recent works in constrained RL have developed methods …

Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for
cooperative partially observable environments focus on finding factorized value functions …

We study off-policy evaluation and learning from sequential data in a structured class of
Markov decision processes that arise from repeated interactions with an exogenous …

We consider the challenge of policy simplification and verification in the context of policies
learned through reinforcement learning (RL) in continuous environments. In well-behaved …

Diffusion models are a powerful class of generative models capable of mapping random
noise in high-dimensional spaces to a target manifold through iterative denoising. In this …

Although deep reinforcement learning (DRL) has many success stories, the large-scale
deployment of policies learned through these advanced techniques in safety-critical …

A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due
to the policy gradient theorem, which provides a simplified form for the gradient. The off …

The development of Policy Iteration (PI) has inspired many recent algorithms for
Reinforcement Learning (RL), including several policy gradient methods that gained both …